Our model is designed for stock recommendation, where companies with high absolute cumulative abnormal return expectations can be given to traders for their information.

it can be useful to have a confidence threshold beta in the model, so that only companies with p(y = 1|d f ) > β or p(y = −1|d f ) > β are recommended.


Given random variables X ∈ {x 1 , ..., x m }, C ∈ {0, 1} and Y ∈ {0, 1}, if This theorem indicates that a perfect context feature for a given task is not necessarily the class label itself.

even if we have little information about Y , we still have the chance to know the information of P (Y |X) by P (C|X).


The second depends on the context feature only but not on f .

if we fix the context feature C to select the function f , using Corr(P (Y = 1|X), f (P (C = 1|X))) 2 (correlation with the gold standard P (Y = 1|X)) and Corr(P (Y = 1|P (C = 1|X)), f (P (C = 1|X))) 2 (correlation with the "silver" standard P (Y = 1|P (C = 1|X))) produce the same result.


Obviously, for these function words, in the equation both numerator and denominator tend to be smaller than the class-indicative words such as "Xgene" mentioned in Section 1, but the ratio of them may be larger.

it will be interesting to investigate what happen if both numerator and denominator approach to zero.


As a consequence, this could lead to a violation against the initial intuition that each word should have the same importance for learning the transformation matrix.

we propose a second normalization after the mean centering.


Hence we preserve the specialized corpus features albeit with less corresponding weight features in the transformation matrix.

the new concatenated word vector carries self-contained information from both corpora ( Figure 2).


The traditional compositional approach (CA) has better accuracy than CMCBP because the candidates are ranked by their frequency in the target language corpus while the candidates in CMCBP are ranked by the similarity measure, so theoretically those translated by CA are also translated by CMCBP but with the same similarity score (1.0).

the final rank in CMCBP is to some degree randomized as multiple top candidates have the same score.


On the other hand, such strength of tags enables them to act as strong search keywords and efficient features for recommendation engines (Lambiotte and Ausloos, 2006;Szomszor et al., 2007;Li et al., 2008;Borne, 2013).

websites for different medias like photography 1 , literature 2 , film 3 , and music 4 have adopted this system to make information retrieval easier.


Tags for movies often represent summarized characteristics of the movies such as emotional experiences, events, genre, character types, and psychological impacts.

tags for movies became remarkably convenient for recommending movies to potential viewers based on their personal preferences and user profiles.


Conversely, values for BE5 categories range in the interval [1,5] where '1' means "absence" and '5' means "most extreme" expression of the respective emotion.

3 the VA(D) and BE5 formats are conceptually different from one another insofar as VA(D) dimensions are bi-polar, whereas BE5 categories are uni-polar.


In general, an FFNN consists of an input layer with activation a (0) := emo s (w i ) ∈ R |s| followed by multiple hidden layers with activation a (l+1) := σ(W (l+1) a (l) + b (l+1) ) where W (l+1) and b (l+1) are the weights and biases for layer l + 1 and σ is a nonlinear activation function.

since the emotion formats under scrutiny capture affective states as real-valued vectors, the activation on the output layer a out (where out is the number of non-input layers in the network) is computed as the affine transformation our model differs from the other approaches presented in this section by sharing model parameters (weights and biases of the hidden layers) across the different dimensions/categories of the target format with only the last layer having parameters which are uniquely associated to one of the outputs (see Equation 4).


First, the number of pairs of data sets with substantially overlapping entries is rather small since researchers focus mainly on acquiring ratings for novel words instead of gathering annotations anew for ones already covered.

employing ISR comparison with human performance is only possible on few data sets.


The whole processes is repeated (typically 100 times) with random splits before averaging the results from each iteration (Mohammad and Bravo-Marquez, 2017).

an important difference between SHR and ISR is that the former is computed on a single data set whereas the latter requires two different data sets with overlapping items.


However, 20 raters are often used in psychological studies (Warriner et al., 2013;Stadthagen-Gonzalez et al., 2017b), while being way higher than the number of raters typically used in NLP for emotion annotation, both for the word and sentence level (Yu et al., 2016a;Strapparava and Mihalcea, 2007).

we argue that this choice constitutes a rather challenging line of comparison for our system.


We can interpret cases where the former outperforms the latter as the model agreeing more with the gold data than two random groups of ten annotators would agree with each other.

for these cases we say our model achieves super-human performance, as it cannot be expected that a well-conducted annotation study leads to more reliable results.


In the crosslingual set-up, we make use of the fact that our model does not rely on any language-specific information, since the categories/dimensions describe supposedly universal affective states rather than linguistic entities.

models trained on one language could, in theory, be applied to another one without any need for adaptation.


Furthermore, it is a platform for observing written interactions and conversations between users (Ritter et al., 2010).

social media is a ideal medium to observe and study the expression of vulgar words and, in addition, allows us to study the socio-cultural context of this phenomenon.


However, the Grammar Matrix provides a particularly suitable framework for our analyses because it requires its libraries to be typologically robust.

multiple analyses must be developed for a particular phenomenon to account for its cross-linguistic distribution, and these analyses must interact with other phenomena in the customization system in order to produce customized grammars for any given language.


As described in this section, because adverbs are not syntactic heads, many features will not be passed up the tree.

rather than adding constraints on the matrix clause to the adverb's lexical type, we add them to the clausal modifier via a non-branching rule and use a new feature, SUBORDINATED to constrain the non-branching rule's daughter to contain the appropriate adverb.


MRS composition is done locally at each level in the tree, and though the adverb has access to the semantic head of the subordinate clause (the verb), it doesn't have access to any information about the matrix clause.

we add the predication and related semantic constraints with a unary rule, after the clause is fully formed.


The SRNN is 136 times as fast as the standard RNN, and the speed advantage may be even bigger when longer sequences are used.

sRNNs may achieve much faster speeds on long-sequence tasks such as speech recognition, character-level text classification and language modeling.


For the training of MTL models, we make sure that each mini-batch is balanced; the difference in numbers of examples from any pair of tasks is no more than 1.

each epoch may not go through all examples of some tasks whose training set sizes are large.


The purpose of this contrived task is simply to demonstrate the variability that can result from the random partitioning in K-fold CV.

"true performance", consists of the global score on the held-out reviews.


Sub-optimal tuning is a consequence of this large variance, which overshadows any potential problems from the bias.

we first need to reduce internal variability before our tuning can benefit from reduced bias.


In our experiments we do not observe improvements by this method.


However, these bilingual resources are very expensive for low-resource languages like Uyghur.

in this study we follow the work described by (Vulić and Moens, 2015) to obtain cross-lingual word embeddings with comparable data.


The transliteration model assumes that source and target characters are generated monotonically.


When borrowing, a word in the donor language may adopt the receipt languages pronunciation system.

the pronunciation of a loanword belongs to two different pronunciation systems (receipt and donor).


The test set of the Uyghur part was annotated manually, which include loanword information in four donor languages.

we used the test set both for evaluation of loanword identification and NMT.


If a nonterminal A occurs more than once in a run of the chart, then the sum of the probability of all runs that contain A is not equal to its expected frequency α(A ) • β(A ).

the above formula is reasonable under the assumption that each nonterminal A in N Ga occurs at most once in a run on a derivation tree in L(G f a ).


If we merge A 1 , A 2 , and A 3 to A and project weights according to the max-rule-product principle, then we obtain the CFG G c a with weights Now the weight of some parse tree of G c a is exponential in the number of occurrences of the chain rules A → A it contains.


Each path in the decoder take encoder's double-path outputs as context to make attention.

there are four types of information flows through encoder to decoder.


Our analysis focused on three possible sources of parsing errors, namely limited training data, part-of-speech (POS) tagging errors, and ambiguous constructions.

we found that the last two sources, which frequently appear in Vietnamese text, significantly attributed to the poor performance of Vietnamese parsing.


While similar problems also occur in Chinese (Xia et al., 2000), parsing Vietnamese may be more difficult because the modern Vietnamese writing system is based on Latin characters, which represent the pronunciation but not the meanings of words.

there are many polysemous expressions in Vietnamese, i.e., expressions having the same surface form with different interpretations.


However, it should be noted that the reductions were not significant, especially in the cases of VP and NP attachments.

enhancing POS tagger would somehow improve the performance of Vietnamese parsing but the improvement would not be radical since VP and NP attachments are the most frequent errors.


Since values of P n are always smaller than 1 and k = 100, and knowing the partition function at the beginning of training is large for all contexts, we can assume that r θ (ŵ i |H) will be large for a least part of the set of noise samples (ŵ i ) k i=1 .

this second term is the largest part of the objective, whose minimization leads to a decrease of the model scale.


Besides, if the noise distribution is correlated with the data distribution, an example with a high value of P n tends to be more frequent.

the model will be able to learn first from words w with the higher values of P n (w).


There is a trade-off in the choice of P n : the closer P n is to the data distribution, the lower is the estimation error; however the Zipfian distribution of word frequencies implies too few updates for rare words if P n is the usual unigram distribution.

p n is often distorted into p α n , with a parameter 0 ≤ α ≤ 1.


Research in the development of MT has been conducted for technologically favored and economically as well as politically important languages of the world since the 17 th century.

notable progress towards the development and use of MT systems has been made for these languages.


To avoid words with the same meaning from being taken as distinct words due to these character variants, we have replaced a set of characters with similar function into a single most frequently used character.

of normalizing character variants in Amharic text , reduction in the number of word types (vocabulary) has been obtained.


Related characters are already grouped and connected to each other in subwords, so there is no need to decompose and then glue them back together.

our basic unit for translation is a morpheme (subword).


The translation generated on the target side could appear in different surface forms but it should convey the core meaning dictated by source-side stems.

to generate a translation the minimum requirement is stem information.


Another important feature of document-level prediction is that certain parts of a document may be more important than others, such sentences containing keywords in a news article, versus sentences containing background information.

one should also attempt to assess whether those sentences in particular are translated accurately.


This procedure is particularly harmful for DE-EN BLEU and WBLEU scores (for BI-RNN, average ∆ρ=0.11), which is the most well represented language pair across WMT years.

it may be the case that the random ALL selection contains more useful data.


However, in dependency parsing situation, the candidate set of the head words is subject to the words in the sentence.

the predicted head would likely fall outside the input sentence when decoding with a full vocabulary upon corpus.


Furthermore, the results on row 4 (HMLSTM + Schedule + LN) are better than the reproduction results which also implement the CopyLast feature.

in the rest of the experiments, we consistently add layer normalization on all layers as well as learning-rate scheduling, and drop CopyLast.


Effectively extracting character-level features from words is crucial in many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), Part-of-Speech (POS) tagging, and Slot tagging.

most state-of-the-art methods for these tasks exploit some kind of character-level features dos Santos and Zadrozny, 2014).


(2016) showed that small batch sizes lead to more global and flat minimizers, while large batch sizes lead to more local and sharp minimizers.

starting from a small batch size and increasing it during training would result in a more global, but sharp minimizer.


Neural machine translation (NMT) models based on the encoder-decoder model, also known as the sequence-to-sequence model (Sutskever et al., 2014), have successfully shown their quality translation.

various NMT models are studied in the field of machine translation.


NEs could be considered as a kind of semantic category of words/phrases.

nEs have contributed to many nLP tasks, including SMT.


We conjecture that chunking increases vocabulary sizes.

the naive incorporation (i.e., Baseline Chunk) might suffer from the data sparseness problem, whereas Proposed IO might alleviate the sparseness problem by the hierarchical structure of the encoder (i.e., word-level LSTM layer).


It can be found that deconvolution owns the potential to provide global information for guidance of decoding.

we follow this idea and propose a new model with deconvolution for NMT.


To get around this we run through the titles, and for each title, we split it into its constituent words and save the rest of the title in a file whose name is the first word.

all titles that begin with a particular word are clubbed into a single file and for those titles that are of length one, we add an empty line into the corresponding file.


However, these methods are subject to the amount of labeled data and the limited information provided by contexts.

we attempt to apply transfer learning and knowledge base to address these issues.


The greater the difference between the experimental results, the greater the difference between source and target datasets.

we notice that Yahoo QA is the most different from SQuAD-T, and InsuranceQA is also very different from SQuAD-T, while TREC QA is the most similar one.


We also allow relations to attach to other nodes rather than the q-node, enabling longer relation paths between a known entity and the q-node.

the semantic graphs defined here are a superset of the query graphs in Yih et al.


But even if we adopted some precise definition of cloud boundaries, so that it became meaningful to say that this cloud is exactly N cubic yards in size, not N + 1 or N -1, it might still be beyond mankind's abilities actually to measure clouds so exactly.

of the fuzziness of human language, the criteria defined in an annotation guideline can be quite vague and be left to annotators' interpretation, which can result in a low IAA.


Indeed, such methods can artificially restrict the annotators' language interpretation and their opinion about sentences' goodness.

the methods do not catch the model's ability to generate language variability.


Recurrent networks provide a natural solution for modeling variable length sequences.

we use bi-directional Gated Recurrent Units (BiGRUs)  as the main building blocks for encoding the given document and query.


Fact-checking on the other hand "addresses the claim's logic, coherence and context" (Mantzarlis, 2015).

verification is a necessary and crucial first step in the process of fact checking as it assesses the trustworthiness of the contexts considered.


Furthermore, it is often grouped together with the term "hate speech" as in the case of recent legislation (Deutsche Welle, 2017), despite the latter being more related to the use of emotive language instead of truth assessment (Rahman, 2012).

we avoid using this term in this survey.


The increased demand for fact checking has stimulated a rapid progress in developing tools and systems to automate the task, or parts thereof (Babakar and Moy, 2016;Graves, 2018).

there has been a diverse array of approaches tailored to specific datasets, domains or subtasks.


The RTE-based models assume that the textual evidence to fact check a claim is given.

they are inapplicable in cases where this is not provided (as in HeroX), or it is a rather large textual resource such as Wikipedia (as in FEVER).


The general assumption is that true and false rumours would have different patterns in the stance distribution over time.

after training models for true and false rumours, we can build a binary veracity classifier by comparing sequence occurrence probabilities for the two cases.


However, three of these events evoked less than five rumours consisting of five or more tweets.

we limit our experiments to the events detailed in Table 1  Each of the remaining events consists of several rumours and each rumour of several tweet threads.


how many tweets are necessary to achieve a reasonable classification performance.

we also explore early detection of rumours by confining our systems to the first ten (first five) tweets only during classification.


As it is also indicated by the comparably lower precision scores, model λ's produces more false positive classifications than the other models, especially concerning the events Sydney siege and Ferguson (B1: 15; B2: 14; λ: 18; λ : 26).

all 15 misclassified rumours are false positives.


However, it is also reasonable to assume that events are heterogeneous in their stance distribution patterns, which might have an impact on classification performance and generalizability across events.

in subsequent analysis different event types should be considered when training MSHMM.


The various model choices for the S module include Temporal Convolutional neural networks(Kim, 2014) (CNN) and sequential models like Long Short-Term Memory (Hochreiter and Schmidhuber, 1997) (LSTM) and Gated Recurrent Unit   Let v i ∈ R d be the d-dimensional word embedding of the i th word of a sentence of length n. We show 3 different models to produce a sentence embedding from the word embeddings.

then, the S module can be represented mathematically as a composition function f that takes a sequences of n word embeddings as input to produce a sentence embedding s. where the choice of the composition function are standard generic neural networks like LSt M, GRU, CN N .


However, most studies have depended heavily on labeled training data (Tutek et al., 2016;Liu et al., 2016).

these methods had difficulty in predicting a stance toward an unseen topic.


We exclude topics that do not exist in the data used in our study.

we used 221 topic pairs as evaluation targets.


Using our proposed method, it is possible to target these users by considering their posts.

our method is useful for analyzing opinions, including those of the silent majority.


In addition, FMs used in this work are advantageous because various types of features can be used simultaneously in an input matrix.

it is expected that social media specific features such as users' follow-follower relationships, retweet relationships, or profile could be useful information for predicting stances.


However, this is not a straightforward task, as external sources might not be available, particularly for just-published news items.

the fact-checking approach is predominantly useful for the detection of deception in texts for which external, verifiable information is available.


However, since both tasks deal with deceptive content, we hypothesize that there are linguistic aspects that might be shared between these tasks.

we focus on the linguistic approach and build upon an emerging body of research on computer-automated verbal deception detection (Fitzpatrick et al., 2015).


To simplify the fake news production task (and also address the latter challenge), we also opted for working with a shorter version of the original news article.

we manually select a news excerpt -about two or three paragraphsthat summarizes the news article.


During the initial stages of the data collection, we noticed that celebrity news tend to center on sensational topics that sources believe readers want to read about, such as divorces, pregnancies, and fights.

celebrity news tends to follow certain celebrities more than others further limiting topic diversity in celebrity news.


Next, seeking to investigate whether larger amounts of training data can improve the identification of fake content, we analyzed the learning trend of our best classifiers.

we plot the learning curves of the LIWC features, readability features, and the combination of all features sets using incremental amounts of data as shown in Figures 1a and 1b.


In addition, we evaluate the performance of the automatic fake news classifiers against the human capability to spot fake news.

we compare the accuracy of our system to that of human annotators.


To compare both types of content, we subtract the average percentage of words in each LIWC category in the fake news from its corresponding values in the legitimate news set.

a positive result indicates an association between a LIWC class and legitimate content, and a negative result indicates an association between a LIWC class and fake content.


Nevertheless, while linguistics features seem promising, we argue that future efforts on misinformation detection should not be limited to these and should also include meta features (e.g., number of links to and from an article, comments on the article), features from different modalities (e.g., the visual makeup of a website using computer vision approaches), and embrace the increasing potential of computational approaches to fact verification (Thorne et al., 2018).

future work might want to explore how hybrid decision models consisting of both fact verification and data-driven machine learning judgments can be integrated.


This leads to the circulation of rumours, claims that are unverified at the time of posting, and for which eventually evidence proving their true or false nature may come out (DiFonzo and Bordia, 2007).

a rumour resolution system needs to go through a set of steps, from detecting that a new circulating claim is a rumour, to the ultimate step of determining its veracity value.


once rumour detection is performed, the information classified as a rumour is then fed into the the stance and veracity classification components of a system to ultimately determine the veracity of the rumour.


Whereas Alonso and Plank (2017) were considering low-level linguistically related tasks as auxiliary tasks, such as part-of-speech tagging, we are working with higher level theme-related tasks.

it is interesting that we still observe similar trends when looking at the same data properties.


representation, and the aim is to learn vector representations for such data.

the notion of reconstruction error used in SDL does not apply to SWESA and hence the optimization formulation used is significantly different from the one used in SDL.


In contrast, the WordList model and Learner+WordList model retrieved texts with 80.60% and 86.26% of non-complex words, respectively.

they succeeded in finding more readable texts, with above 80% comprehension rate, for the beginners.


we can obtain a set of phrases annotated with the same image region.


The number of clusters (i.e., the number of paraphrase sets in a set of an image and captions) is not explicitly given in our task.

we apply the affinity propagation algorithm (Frey and Dueck, 2007) to cluster entities, which can estimate the number of clusters as well.


When connecting the parent and child nodes, the directed edge indicates that the child is an important discourse unit in the relationship, and the undirected edge indicates that the child is secondary in the relationship.

in Figure 1, there is an Evaluation relation between the discourse units P1 and P2, and they form a higher level unit (relational node) DU1-2.


However, RST-DT focuses on the micro discourse level, which is only tagging the relations on the sentence level and ignoring the relations on the paragraph level.

it leads to some tailoring and correction when Sporleder carries out macro discourse analysis on the paragraph level.


Second, different from micro discourse relations, there are no connectives between the macro discourse units with lengths that are longer than those of micro discourse units.

it is difficult to grasp the theme of macro discourse units and the relationship between them.


Considering the overall intentions of a news article, it is rare to have a Transition relation on the macro-level, and most of them have a Contrast relation.

based on the micro discourse relation representation that has 4 categories and 17 relations defined by CDTB, we delete the relations, such as Transition, Concession and other relations in the Transition category, and finally form the macro discourse relationship representation with 3 categories and 15 relations, as shown in Table 2.


The topic sentence of a paragraph, the lead and the abstract of a full text are related to the theme and key points of the text and are the overall structure of semantics, which also belong to the macro discourse structure.

in addition to the traditional annotation of micro discourse corpus, we also annotated the additional macro discourse information, including the topic sentence of a paragraph, the lead and the abstract of a full text.


In the annotation of a macro discourse relationship, the leaf nodes are natural paragraphs and do not need to be manually divided.

unlike the assessment method of the RST-DT agreement, we do not use the leaf nodes as an example of a consistent assessment.


However, the compressed sentences might be grammatically incorrect and not ordered appropriately to be coherent.

we select the right set of compressed sentences along with their order via an Interger Linear Program (ILP) formulation by extending the framework used by Deshpande et al.


In other words, the word sequence needs a simple classification as the label sequence does, and the label sequence can benefit from the semantic information of the word sequence.

we fuse the disfluency detection sequence and the label sequence through multi-task learning to improve the performance.


Different from repeating operation which simply repeats words in the sentence, we insert words randomly selected from a top frequent dictionary, which is extracted from the corpus by keeping the top 10,000 most frequent 1-grams to 5-grams respectively beforehand.

inserting operation can introduce more complicated noises.


Different from (Cheng et al., 2016;Saha et al., 2016) which adopt the fully shared encoder, we share only partial weights for the encoders, considering that 1) The independent weights are devoted to encode the hidden features about the internal characteristics of each corpus, such as the terminology, style, and sentence structure; 2) The shared weights are concentrated on projecting those hidden features into the shared-latent space.

we share the weights of the last few layers of encoders Enc l and Enc u , which are committed to capture the high-level representations of the input sentences.


However, there is no reference training set for the standard, since the only public resource currently available with ISO 24617-2 annotation (DialogBank, (Bunt et al., 2016)) is too small to be used to train classifiers.

most DA tagging research still focuses on in-domain studies on large datasets with incompatible DA annotations (Stolcke et al., 2000;Ji et al., 2016).


Given the limited -and often domain-dependent -annotation scheme of these resources, it is impossible to map enough data to train a DA tagger for the full ISO taxonomy, since some of the ISO dialogue acts have no correspondence in any of the considered corpora.

we opted for a reduced version of the taxonomy, limiting our research to subsets of the General (Task), Social Obligation Management and Feedback dimensions.


Additionally, unlike for SWDA, the addition of word embeddings with and without index dependency relation (I-DEP) does not produce significant improvements for all but CAPC.

the model trained on 1-2-grams, previous DA tags, indexed POS-tags and dependency relations is chosen for the ablation study.


Schematic pictorial content abounds in everyday communication, from informal interactions around a whiteboard to figures in formal scientific and engineering documents.

diagrams have been studied from the standpoint of design, as in Horn (1998); psychology, as in Larkin and Simon (1987); computer graphics, as in Agrawala et al.


Owing to the uncertain range of optical flow displacement, normalization is applied to constraint the displacements in [0, 255] which is same as gray image.

an optical flow field can be regarded as an image with 10 channels in the motion stream.


We can also find that LSTM-CSS and LSTM-CSS-E nearly perform equally.

we believe that attention mechanism can make the LSTM translation network focus on relevant content and alleviate the influence of the places where local constraints are input.


Fully-shared modeling can share knowledge between tasks or languages based on task-invariant components and language-invariant components, however, classification performance in some data sets is deteriorated.

this paper proposes the partiallyshared modeling strategy; it introduces not only shared components between tasks or languages but also exclusive components that handle task-language combinations.


We then replace these class symbols with slot values appropriate to the target culture.

unlike the first two systems, the class-based translation model is an adapted translator.


The SKR criterion itself does not impose constraints on translation quality.

it needs a welltrained general-purpose NMT system as the initial model.


Though it is a useful prediction strategy, depending on beam width, beam search more or less suffers from its nature of focusing solely on current optimal results.

beam search tends to ignore some partial sequences which might lead to better future outcomes, especially if its beam width is too small.


By contrast, in the large-width beam search, we have a higher chance to observe more top-K s of C K l t+1 generated from different l k l t , since there exist more potential candidates l k l t with high future probabilities p(w|l k l t , x).

the larger beam width leads to a higher diversity in generated sequences generally.


Although a large-width beam search generates responses with higher probability and diversity, increasing beam width proportionally slows down the inference process.

to retrieve better responses and meanwhile maintain the inference efficiency, we propose Prospective-Performance Network to estimate the future rewards of partial responses in the inference procedure of NRG.


In practice, given one partial response and its query, its future reward is unknown without results from a large-width beam search as references.

we propose Prospective Performance Network (PPN) as a future reward estimator.


• Length Component: In general, the information provided by short and long partial responses is significantly different from each other.

the Length Component is created to summarize the length information of partial responses, it transfers the response length into a length vector l by a length embedding matrix L, different for each time step.


As mentioned in section 2.2, top-K s responses from K l -width beam search benefit from properties of higher probability and diversity.

k here is set as k s , so that a partial response with positive future reward is also associated with above desirable properties.


S ← S ∪ C complete 10: until |S| = K s or t = L 11: Output: y = argmax y∈S∪C log P (y|x) In the decoding step of Seq2Seq, its time complexity is dominated by the vocabulary size |V |, because of the large-scale computation in the operation of probability distribution projection; while the time complexity of implementing PPN is much smaller than that of decoding, with the help of the simple structure of PPN.

the time complexity of incorporating PPN in K s -width beam search is on the same scale as that of the vanilla K s -width beam search, which means incorporating PPN into beam search preserves the inference efficiency.


For each language, the feature extractor f E receives an input conversation x and computes the hidden features h = f E (x) 7 .

by using f E , we obtain the hidden feature h (s) and h (t) for English and the others, respectively.


The Visual Genome is a knowledge base that combines metadata from different sources about the images it contains.

future researchers can use information from all these different sources in their analysis of our data.


The schemas therein generated are not directly evaluated.

we draw a line after chainsim and other techniques used on the cloze task, referring to them as the candidate score.


Some skills, such as question generation and text summarization, process input paragraphs and generate responses for further usage.

the DM can choose from these responses to send a message to the user.


A user or a bot can start the conversation.

at this state, the bot waits for the first user message or executes the greeting skill if there is no message from the user after some time.


It helped us to avoid using rules and simplified conversational agent's development.

of introducing the above-mentioned changes, our bot won the Conversational Intelligence Challenge Finals (Table 1) Table 1: Results of the Conversational Intelligence Challenge Finals.


AMRs capture basic representations of semantic roles (using the numbered arguments of PropBank (Palmer et al., 2005)), as well as within-sentence coreference, named entity and entity linking information.

a simple sentence such as "Bill left for Paris" can be represented as in the first AMR in Figure 1, with "leave-11" denoting the physical departure sense of "leave", numbered arguments such as "arg2" denoting semantic roles that are unique to that sense (e.g.


Annotators start with gold AMR annotations (using the upcoming AMR public release), and add coreference relations (and related annotations such as bridging and implicit roles) as a layer on top of those gold AMRs.

rather than annotating spans, annotators label clusters of variables that refer to the same entity or event over the AMRs constituting a document.


Not all words contribute equally to the representation of the argument.

we conduct an attention pooling operation over all the words to get the contribution of each word.


For example, "animal" is a hypernym of "cat".

"A person feeding an animal with a banana" cannot be a valid generated contrastive adversarial caption for the image with the caption of "A person feeding a cat with a banana."


We extend each caption with 60 adversarial samples (20 noun-typed, 20 numeral-typed and 20 relation-typed).

each image has 60 × 5 contrastive adversarial samples in total.


Our joint embedding model maps authors, attributes, and text into the same space.

it allows us to compute the similarity between any pair of authors, texts, attributes, or their combination.


So they will get updated according to L pred .

we do not include L AR and L T R explicitly in the global loss function.


As shown in Figure  1, the last utterance in a conversation empirically conveys the user intention while the other utterances depict the conversation in different aspects 1 .

instead of considering all the conversation turns equally, we have to weigh previous conversations in a more sophisticated way.


But a complex data set like set of web-snippets can be clustered into several alternative partitionings.

to detect all possible partitionings containing clusters of different shapes automatically, application of multiobjective optimization (MOO) for solving the problem of clustering becomes prevalent (Maulik et al., 2011).


For example, words that contribute to the syntax rather than the meaning of a sentence should have lower impact on clustering algorithm compared to more specific rare words.

we scale each word vector by the corresponding TF-IDF weight for that word and generate the textual view following the approach in Section 3.1.


child-Sum Tree-LSTM and GcN have stronger preference compared to the all comments case.


Our own corpus coverage analysis shows that 47% of word types (277K) occur only once in the corpus, which is compatible with Boun Corpus word coverage statistics (Sak et al., 2011).

we couldn't ignore the words that have zero or less than five frequencies.


The arbitrariness of this technical solution is made clear by the fact that, by adopting it, the coreness of a coordinate structure depends on the order of conjuncts.

in "We asked the president about the hunt for Osama bin Laden and whether Americans are safe at home", the coordinate dependent would be classified as non-core, because the first conjunct is now an obl dependent of asked.


Also, in both cases an enhanced relation points at the subject of secondary predication (if locally present).

the analysis is essentially the same, whether the secondary predicate is considered core or non-core, with the only difference being the name of one dependency label (xcomp or xadvcl); the following representations should be compared to (9)-(12) in the main text.


58he asked her for a kiss and to go As in the case of the previous dependency structures, the differences between the basic representation (at the top) and the enhanced representation (at the bottom) are marked in red.

while the enhanced representation contains dependency edges from asked to particular conjuncts, labelled as obl:n (for a kiss) and obl:x (to go), the basic representation contains just one edge labelled as obl.


Consequently, shifting to a new domain requires the user to not only name the target relations, but also to manually define new extraction rules or to annotate new training data by hand.

those systems rely on extensive human involvement.


Therefore, they introduce a more semantically-oriented sentence representation that is generated by transforming a dependency parse tree into a directed graph which is tailored to directly represent the proposition structure of an input sentence.

extracting propositions from this novel output format is straightforward.


Aforementioned Open IE systems lack the expressiveness needed for a proper interpretation of complex assertions, since they ignore the context under which a proposition is complete and correct.

they do not distinguish between information asserted in a sentence and information that is only hypothetical or conditionally true.


In fact, those approaches benefit from extractions that are as compact as possible.

minIE aims to minimize both relational and argument phrases by identifying and removing parts that are considered overly specific.


This lack of a well-defined, generally accepted task definition prevented the creation of an established, large-scale annotated corpus serving as a gold standard dataset for an objective and reproducible cross-system comparison.

to date, Open IE systems were predominantly evaluated by hand on small-scale corpora that consist of only a few hundred sentences, thereby ignoring one of the fundamental goals of Open IE: scalability to large amounts of text.


In order to serve for semantic tasks, it is beneficial for Open IE systems to extract compact, self-contained propositions that do not combine several unrelated facts.

systems should aim to generate valid propositions with minimal spans for both relation and argument slots, while preserving the meaning of the input.


In addition, the proposed models are trained using the maximum likelihood estimation method and the cross-entropy loss function, which requires humans to predefine the order of the output labels.

the sorting of labels is very important for the models' performance.


The traditional BR method predicts all labels at once only based on the sample input.

it tends to ignore some true labels whose feature information contained in the sample is insufficient.


The SGM model generates labels sequentially, and predicts the next label based on its previously predicted labels.

even if the sample contains less information of some true labels, the SGM model is capable of generating these true labels by considering relevant labels that have been predicted.


For instance, the correlation coefficient between E51 and E512 is 0.7664.

these highly correlated labels are likely to appear together in the predicted label sequence.


The proposed SGM captures the correlations between labels by utilizing LSTM to generate labels sequentially.

for some true labels whose feature information is insufficient, the proposed SGM is still able to generate them by considering relevant labels that have been predicted.


These represent infinitely many features, and are not generated explicitly; only those present in the data are generated, on the fly, as part of the learning process.

it yields hypotheses that are as expressive as relational learners in a scalable fashion.


The output of the actual taggers (MBT and MXPOST) is not suitable to study the proper effect of the relevant issues of information source and algorithmic parameterisation, since different information sources are used for each tagger.

the taggers need to be emulated using symbolic learners and a preprocessing front-end to translate the corpus data into feature value vectors.


Given this, a generic system is required to facilitate the analysis of behavioural trends amongst selected pairs of terminals and non-terminals alike, regardless of the target language.

an intermediate research goal is to apply Natural Language Learning techniques to the identification of "higher-level" lexical and grammatical patterns and structure in a linguistic signal.


Each of the nodes in this part of the hierarchy is represented by a boolean feature which is set to 1 if that node lies on the path from the root of the hierarchy to a particular semantic class.

for example, the semantic features of a noun in the semantic class organization consists of a vector of 30 features where the features corresponding to the nodes noun, concrete, agent and organization are set to I and all other features are set to 0.


These scores were furthermore treated as frequencies.

high ranked parses would be treated as events occurring more frequently in the training data, and low ranked parses would be treated as occurring rarely.


Ultimately, whether a particular acquisition model is successful is an empirical issue and depends on the exact conditions under which the model performs well and the extent to which those favorable conditions are in line with the facts of human language.

i believe a three-fold approach to validate a computational model of acquisition is warranted.


For the reasons expressed above, we are interested in knowledge-free morphology induction.

in this paper, we show how to automatically induce morphological relationships between words.


Developing a scoring algorithm to compare directed graphs is likely to be prone to disagreements.

we score only the vertex sets of directed graphs.


Rules can be seen as generalised instances; they represent the set of training instances with the same class that match on the conditions on the left-hand side of the rule.

classification strategies from memory-based learning can naturally be applied to rules.


The PS method produces samples from the exact limit distribution and, thus, the sampling mean given in (4) is less biased than the one obtained with the MCMC methods.

we can obtain better estimations of the parameters Ai.


For guessing WS categories only the first stage guesser is relevant, as inflectional patterns are not sufficient for guessing the semantic of a given token.

the ending able characterises very probably an adjective, but does not provide any semantic information 4 on it.


If any uncovered examples set, g~-, remains after FOIL's performance, this is due to the lack of sufficient examples.

the system tries to improve recall by growing set g+ with artificial examples (pseudo-examples), as explained in 3.2.


Ascending tree automata recognize a wider class of languages (Nivat and Podelski, 1!197;G~cseg and Steinby, 1984) and, therefore, they allow for richer descriptions.

our model will compute the expansion probability for a given node as a function of the subtree of depth k -2 that the node generates 1, i.e., every state stores a subtree of depth k -2.


We assumed that Once the integrated transducer has been made, the tagging and shallow parsing process consists of finding the sequence of states of maximum probability on it for an input sentence.

this sequence must be compatible with the contextual, syntactical and lexical constraints.


Unlike the voting algorithms, the classifiers do not require a uniform input.

we have tested if their performance can be improved by supplying them with information about the input of the first classification stage.


3565 new super concepts were introduced due to our disambiguation strategy.


The learning method finds 679 is-a relations, but 161 is-a relations were determined to be wrong by empiric user evaluation.

76,29 percent of all discovered relations were regarded correct by the user.


With different sub-networks used to encode different kinds of linguistic knowledge, linguistic regularities are encoded near the top of a network, while types further down the network are used to represent sub-regularities or exceptions.

types are concisely defined, with only specific information being described, since more general information is inherited from the supertypes.


In Czech corpora compound verb groups are usually tagged in word-byword manner.

some of the morphological tags of particular components of the verb group lose their original meaning.


In annotated Czech corpora, including DESAM (Pala et al., 1997), compound verb groups are usually tagged in word-by-word manner.

some of the morphological tags of particular components of the verb group loose their original meaning.


Actually verb rules make partition of them.

we can tag all these basic verb groups withont the limitations mentioned above.


The SOM was chosen because the input is matched to the unit with the closest weight vector.

it is motivated by the same principle as used to find the closest match in MBL.


This suggests they may be due to noise due to different weight initialisations in training rather than a systematic difference.

at the moment it is unclear whether SOMMBL or LSOMMBL have an advantage compared to each other.


In the class-to-class model 12 examples were tagged correctly and 7 incorrectly.


2. turn-to (Semcor 1, class-to-class 2): the relations "take_up-obj-position" and "call_onobj-esprit_de_corps" introduce noise and point to the class <artifact, artefact>.


All the classes allow the same subcategorizations transitive and intransitive, entailing that they cannot be discriminated by subcategorization alone.

successful classi cation demonstrates the induction of semantic information from syntactic features.


In order to compare our results to the monolingual method on a large corpus as in Merlo and Stevenson, 2001, we also collected the 5 English features for our verbs from the 65M word WSJ corpus.

we h a ve a total of four data sets: English HKLaws dataset, English WSJ dataset, aligned Chinese HK-Laws dataset, and unaligned Chinese HKLaws dataset.


The best single feature in the unaligned dataset is also one of the verb tags, achieving only a slightly lower accuracy of 71.5, SE 0.5.

we h a ve the surprising result that Chinese features alone, from a fairly small dataset, are far superior to the English features from the same bilingual corpus 75.4 versus 49.5 best accuracy respectively.


So the sequence with a fork, which corresponds to only one nucleus is treated as a three word sequence in model C. Apart from this difference, model C directly relies on a combination of equations (10) and 12 .

model C uses a reduced version of equation 12and an extended version of equation 10.


We have experienced that for different reasons some systems perform worse than others and including their results in the majority vote decreases the combined performance.

it is a good idea to evaluate majority voting on subsets of all systems rather than only on the combination of all systems.


Although some work with individual systems in the project has been done with the goal of combining the results with other systems, tuning data is not always available for all results.

it will not always be possible to apply all ten combination methods to the results.


My initial experiment clustered all of the tag sequences in the corpus that occurred more than 5000 times, of which there were 753, using themeans algorithm with the ¤ ¦ ¥ -norm or city-block metric applied to the context distributions.

sequences of tags will end up in the same cluster if their context distributions are similar; that is to say if they appear predominantly in similar contexts.


Figure 1 shows a graph of how the distance between two symbols affects the MI between them.

if we have a sequence of length 2, the symbols before and after it will have a distance of 3, and we would expect to have a MI of 0.05.


Then at the next iteration, this sequence will be examined by the algorithm.

the algorithm progressively probes deeper into the structure of the corpus as syntactic variation is removed by the partial parse of low level constituents.


even adding a linguistically valid rule can degrade numerical performance (sometimes dramatically) by introducing ambiguity to a greater degree than it improves coverage.


However, this would mean that the same noun will be classified in the same way regardless of the text.

three text dependent features were introduced.


(1999), where syntactic NPs are captured for the goal of extracting their semantic content but are processed as an "ordered" string of words rather than a syntactic unit.

we introduce as a separate feature in the feature vector, a new TF*IDF measure which consider the NP as a sequence of equally weighted elements, counting individually the modifier(s) and the head.


Feature that considers all constituents of the NP equally weighted One of the important hypotheses we tested in this work is that both the modifiers and the head of NP contribute equally to its salience.

we consider mh tfidf as an additional feature in the feature vector.


We used a list of 571 common words used in IR systems in order to further filter the list of candidate NPs.

words like even, following, every, are eliminated from the noun phrase structure.


refer to the book vs. *refer the book to).

the existence of a verb-preposition pair in the split configuration is sufficient evidence for a VPC analysis.


In particular, we note that for a remarkably wide range of languages, academic libraries, many booksellers and websites offer a foundation of linguistic wisdom in reference grammars and dictionaries.

starting from this baseline, what is the marginal cost of distilling from and augmenting this existing knowledge to achieve a desired new task functionality?


A substantial percentage of foreign language dictionaries that are available on line or in smaller paperback format are simple bilingual word or phrase translation lists which fail to specify part of speech.

1 one component question of this work is how can one extract preliminary part-of-speech distributions from untagged monolingual translation lists.


the pronominal inflections of mi, tu, su).

as one final amount of supervision, lists of closed-class words, paired with their inflections and fine-grained part-ofspeech tags were entered manually from the grammar book (e.g.


As would be expected given that data entry was done by computer scientists which were not native speakers of the test languages, significant analysis errors or gaps were introduced when rather blindly transferring from the reference grammar.

to test the relative contributions of limited native speaker help when available, for roughly 4 additional total person hours in a second test condition for Romanian a native speaker corrected and augmented gaps in the patterns previously entered from the grammar book, focusing almost exclusively on the complex inflections of closed-class words.


And ultimately the performance is quite  remarkable given that it is the result of less than 1 total person day of data collection and supervision, in contrast to the thousands of hours and $100,000-$1,000,000 spent on some annotated training data in a much more limited tagset inventories.

in terms of cost-benefit analysis, the supervision paradigm and associated bootstrapping models presented here offer quite a good value of new functionality per labor invested.


The later paradigm is not directly usable when analyzing a large corpus in which there are no document boundaries, like the one provided for Spanish.

a segmentation process needs to be employed, so that all the instances of a name in a segment have a high probability of belonging to the same class.


random variables with EX i = µ, µ 2 < ∞, and Var X i = σ 2 < ∞, and define the average Limit theorems codify behaviour as sample size n approaches infinity.

they can only provide an approximate guide to the finite convergence behaviour of the expectation statistics, particularly for smaller samples.


This results in the tendency discussed above to favor clusters consisting of elements of a single dataset.

we define a biased probability distribution, p c (y), to be used by the CD clustering algorithm for characterizing a cluster c. It is designed to call attention to y's that are typical for cluster members in all, or most, different datasets.


Denote this maximal number by M(e j ): M(e j ) = max k =1…7 |{x∈e j : x∈ĉ k ) }| .

the recall measure R is defined to be the sum of the above maximal overlap counts over all expert clusters, divided by all 131 expert terms (repetitions in distinct clusters counted): R = ∑ j=1…8 M(e j ) / 131.


Within these specific religion clusters as well as the other sparsely populated clusters, the ranking inducted by the p(c|x) values is not very informative regarding particular sub-topics.

the IB performs the CD clustering task poorly, even in comparison to random results.


It turns out that the second order model performs about the same as the first order model, but is at a disadvantage because of data sparseness.

we only consider first order models in the rest of this paper.


Their model uses at most three candidates restricted by the grammar as features; the nearest, the second nearest, and the farthest from the modifier.

their model can take longer context into account, and disambiguate complex dependency relations.


does not reflect the actual behavior on actual problems.

in order to evaluate the performance of the optimization techniques sketched in previous section when applied to the problem of parameter estimation, we need to compare the performance of actual implementations on realistic data sets (Dolan and Moré, 2002).


On the other hand, there are elements that often appear in named entities, but are not characteristic for them (like the first name Israel).

a verification step is included in the learning algorithm.


This turns out to be singular; hence their parent Theo gets the default feature firstname.

theophil will correctly be classified as firstname, while the exception theologe will still be classified as title.


The Redwoods treebank makes available syntactic and semantic analyses of much greater depth than, for example, the Penn Treebank.

there are a large number of features available that could be used by stochastic models for disambiguation.


It is not straightforward to detect inconsistencies in the training data by inspecting test data errors.

the percentages we have reported are not exact.


As discussed in Section 3.1, the improvement of the score is not due to the rounds of boosting.


The improvement in the example is clearly caused by multiple stacking.

this MISC entity is allocated tags correctly by LR .


However, if that is to be achieved, we need to concentrate only on algorithms and resources that a human learner could employ.

there are significant constraints on the assumptions that can be made in the learning system implemented.


PP-attachment ambiguity should be resolved by the dependency of three words.

the probability model takes the following form.


In a history-based model, the current decision depends on the decisions made previously.

we may regard parsing as a special form of sequential model without losing generality.


As we have defined them, the training samples are symmetric with respect to the origin in the space.

for any hyperplane that does not pass through the origin, we can always find a parallel hyperplane that crosses the origin and makes the margin larger.


Hence, the outcome separating hyperplane has to pass through the origin, which means that b = 0.

for each test parse x, we only need to compute its score as follows.


e is the space of event of the classification problem, and For classifier g f on space e, its loss function is x 2 =x 1 and g f (x 1 , x 2 ) = +1 0 otherwise the expected risk R vote (f ) for the voting problem is equivalent to the expected risk R class (g f ) for the classification problem.


Ordinal regression searches for a regression function for ordinal values, while our algorithm is designed to solve a voting problem.

the two algorithms differ on the definition of the margin.


It takes about 2 days to train a slice on a P3 1.13GHz processor.

of this subdivision of the training data into slices, we cannot take advantage of SVM's global optimization ability.


Experiments on the development data shows that the result is better if Ae −fiB is much larger than 1.

we may use i f i directly, and there is no need to estimate A and B in (14).


The second class, which we call objective, consists of everything else.

sentences with only mild traces of subjectivity are tossed into the objective category, making the system's goal to find the clearly subjective sentences.


Although 53% accuracy is not high for a fully automatic process, Basilisk depends on a human to review the words so 53% accuracy means that the human is accepting every other word, on average.

the reviewer's time was still being spent productively even after 1000 words had been hypothesized.


There was no require-ment that the web page be focused on that name, nor was there a minimum number of name occurrences.

some pages clustered only mentioned the name in passing, or in a specialized, commercial context (e.g.


Actually, the class c x of x, is given by c x = arg max c∈C P (c|x).

bayes theorem shows that we get In the above equation, P (c) is estimated easily; the question is how to estimate P (x|c).


When a word has multiple thesaurus IDs, we create a feature for each ID.

we get following ten features from the above example sentence: e1=wo, e2=suru, e3=saikou, e3=kako, e4=suru, e4=., e5=3192, e5=31920, e5=1164, e5=11642.


2Whether the EM algorithm succeeds correlates closely with whether the precision is improved by the first iteration of the EM algorithm.

we estimate the optimum iteration number by comparing three precisions, the precision of the iteration number 0, 1 and at convergence.


Note that we cannot use simple raw texts including the target word, because we must use the same dictionary and part of speech set as labeled data.

we use Mainichi newspaper articles for 1995 with word segmentations provided by RWC.


This data is the origin of labeled data.

we gathered 7585.5 and 6571.9 unlabeled instances for per noun and per verb evaluation word on average, respectively.


Furthermore, for 15 of the other 21 evaluation words, the difference between the precision through our method and that through ideal estimation did not exceed 2%.

estimation of CV-EM2 is mostly effective.


On the other hand, the EM method requires Naive Bayes to be used as the supervised learning method, but can be applied to multiclass classification problems without any modification.

the EM method is more practical than Co-training.


An important issue for a statistical machine learning based NLP system is that its performance can depend heavily on the characteristics of the training data used to build the system.

if we train a system on some data but apply it to other data with different characteristics, then the system's performance can degrade significantly.


However, the language modeling problem is essentially unsupervised (density estimation) in the sense that it does not require any annotation.

techniques developed there cannot be applied to our problems.


In this study, we had used a number of datasets that are not annotated.

for experimentation purpose, we have to label each period manually.


From the experiments, we see that the accuracy of all algorithms will be improved to about i l • for all three datasets.

the test set size we have is sufficiently large to distinguish a difference of i l ¥ ¶ ¤ with reasonable confidence.


In the context of k-NN, predicting the class means, typically, that the instance is the closest neighbor for a new instance.

the CPS function is defined as the ratio of the number of times our instance is the closest neighbor for an instance of the same class and the number of times our instance is the closest neighbor for another instance regardless of its class.


A CPS value of 1 means that if our instance is to influence another instance class (by being its closest neighbor) its influence is good (in the sense that predicting the class using our instance class will result in an accurate prediction).

our instance is a good predictor for our class, i.e.


In the unsupervised setting, the large number of potentially irrelevant features becomes a serious problem, since those features may mislead the learner.

the problem of dimensionality reduction is a key issue to be addressed in verb class discovery.


Another striking result is the difference in values, which are very much higher than those for Ling (which are in turn much higher than for Full).

not only do we see a sizeable increase in performance, we also obtain tighter and better separated clusters with our proposed feature selection approach.


To overcome these problems, a class-based approach is used for the collocations, with WordNet high-level synsets as the source of the word classes.

in addition to using collocations in the form of other words, this uses collocations in the form of semantic categories.


In contrast, in a context with an abstract concept (ABSTRACTION#6), the difference in the probability distributions shows that the temporal interpretation becomes more likely.

these class-based lexical associations reflect the intuitive use of the prepositions.


The lowest level of contexts is the concrete instantiation of the stated syntactic skeleton, such as [M ary(S) which are extracted directly from corpora with grammatical lemmatization done during the process.

all word tokens are in their lemma format.


However, each member of a word list can help to disambiguate other members in this list.

it is expected that with the pruning assisted by list members, i.e., the disambiguation by truncating semantic relations associated with each synset in WordNet, the extract meaning in the context together with all other semantic relations such as hypernyms, holonyms, troponyms, antonyms can be derived from WordNet.


We are interested in comparing what most words in begin or end in, rather than emphasizing on the root part, which tends to occur inside the word.

we simply assign an empty value ('-') to those dimensions for short words that are in the middle of the word.


disregarded in the computation of the incoherence.

for each dimension , we obtain a new measure .


From the start we have aimed at tagging the target language dictionary with feature values.

it is clearly not enough to determine which dimensions in the space carry information about a given feature.


a correct noun without a necessary prepositional phrase, a correct noun with a superfluous prepositional phrase, or a noun + prepositional phrase where the object of the preposition is correct but the preposition itself and the noun to which it attaches are superfluous.

these hyponymy relations are potentially correct but will require potentially difficult processing to extract an exactly correct relation.


Comparing this 58% precision with the 40% precision obtained on a random sample in Section 2, we determine that LSA achieved a 30% reduction in error (see Table 3 for a breakdown of annotation results by author).


LSA provides broad-based semantic information learned statistically over many occurences of words; lexicosyntactic hyponymy extraction learns semantic information from specific phrases within a corpus.

we have benefitted from combining local patterns with statistical in-formation.


(BNC) might suggest that nutmeg, cinnamon, and coriander are also spices, because they appear to be similar to cloves.

we can learn the hyponymy relations nutmeg < spice, cinnamon < spice, and coriander < spice that are not directly attested by lexicosyntactic patterns in our training corpus.


All of these three basic operations decrease the description length.

our overall algorithm also decreases the description length monotonically and stops when all three operations cause no decrease in description length.


This operation thus reduces the number of classes in Ì and accordingly reduces the number of parameters in the model.

if we properly choose the 'redundant' classes in a partition, this merging reduces the description length by the greater reduction in the model description length which surpasses the loss in log-likelihood.


We restricted possible dependee bunsetsus to be those to the right of the input bunsetsus because in Japanese, basically all dependency relations are from left to right.

our test data is in the form noun-pp pred ½ pred Ò where pred ½ ,...,pred Ò is the set of all candidate dependee bunsetsus that are to the right of the input dependent bunsetsu noun-pp in a sentence.


Our training data is in the form Ö, noun, pp, pred .

our decision rule given test data (Eq.8) is, to select pred where is the index which maximizes the value È •´n oun pp:pred µ È´ pp pred µ We extracted the training samples and the test data from the EDR Japanese corpus (EDR, 1994).


In these tables, we can see that our method works slightly better than Li's method as an optimization method which minimizes the description length, and also in the evaluation tasks.

we can say that our method decreased computational costs without losing accuracy.


It is often the case with minor words that the number of occurrences does not reach one thousand even in a one-hundred-million word corpus.

constructing a very very large corpus (containing  6 Related Work  reported that the accuracy of text classification can be improved by a large pool of unlabeled documents using a naive Bayes classifier and the EM algorithm.


Other research also appears to support this conclusion (Clark and Porter, 1995).

we are investigating event correlations as a more appropriate and extractable knowledge structure.


First, the Similarity criterion states that each node in an event must be textually similar to at least one other node in the event.

a node must be similar to at least one node that is already part of an event in order to be added to the event.


Second, if the stemmed form of a node's action is "to be", the action is ignored in determining similarity.

the node's agent and theme must match the other node for the nodes to be similar.


To test for this cooccurrence, we extend the definition of cooccurrence to apply to mixed node and event arguments.

c(N, E) or c(E, N ) is the number of times node N cooccurred in the training data with any node from event E. The order in which the arguments are given is the order in which An example of two nodes (N 2 and N 3 ) that meet our two criteria is given in Figure 3.


Fulfillment of the criterion using one neighbor does not ensure that the node will be acceptable given a second neighbor.

if a second neighbor to this event is added to the sequence, the REVISE algorithm removes nodes that fail to remain acceptable.


While standardized lexicons have been investigated (Kamprath et al., 1998), their use has not become common.

competent event generation is required for success in detecting event correlations.


We assume that the decisions only depend on a limited window of around the current word and on the two predecessor tags.

we obtain the following second-order model: A well-founded framework for directly modeling the posterior probability is maximum entropy (Berger et al., 1996).


The system presented here consists of a replication, with some minor changes, of the system that obtained the best results in the CoNLL-2002 NEE task.

it can be considered as a benchmark of the state-of-theart technology for the current edition, and will allow also to make comparisons about the training corpora of both editions.


Thus, misclassifying words of a correct phrase blocks the generation of the candidate and produces a missed phrase.

we move the begin or end prediction vectors toward the misclassified words of a missed phrase.


Named Entity Recognition 1 (NER) can be treated as a tagging problem where each word in a sentence is assigned a label indicating whether it is part of a named entity and the entity type.

methods used for part of speech (POS) tagging and chunking can also be used for NER.


The memory cell's output passes through a squashing function before being gated by the output gate activation.

the output gate controls the activation flow from cells to outputs.


(1) We avoid dynamic programming for inference in the gain calculation with a mean-field approximation, removing the dependence among states.

(we transform the gain from a sequence problem to a token classification problem.


Much of the research in this field was pioneered in the Message Understanding Conference (MUC) (Sundheim, 1995), which performed detailed entity extraction and identification on English documents.

most current NER systems which have impressive performances have been specially constructed and tuned for English MUC-style documents.


When a non integer solution is given by LPR, it adds a new linear constraint that makes the non integer point infeasible, while still keeps the optimal integer solution in the feasible region.

the feasible region is closer to the ideal polyhedron, which is the convex hull of feasible integer solutions.


Since the basic and pipeline approaches do not have a global view of the labels of entities and relations, 5% to 25% of the predictions are incoherent.


If the relation classifier has the correct entity labels as features, a good learner should learn the constraints as well.

the quality of omniscient is almost as good as LP.


We call If occurrences of word and features are conditionally independent given labels, then C WE is zero 4 .


The major problem of this choice is that there is no linguistic theory that supports the selection of syntactic features to recognize semantic structures.

researchers are still trying to extend the basic features with other ones, e.g.


there is no overlapping between the words of two arguments.

two semantic structures F p1,a1 and F p2,a2 3 , asso- ciated with two different arguments, cannot be included one in the other.


For example, if we change the tense of the verb to deliver (Figure 2) in delivered, the [VP [V delivers] NP] subtree will be transformed in [VP [VBD delivered] NP], where the NP is unchanged.

the similarity with the previous structure will be quite high as: (1) the NP with all sub-parts will be matched and (2) the small difference will not highly affect the kernel norm and consequently the final score.


The head words work and surface are indicative of the calling for stamina/endurance and not easily penetrable senses of hard.

the head word of the phrase housing the target word is used as a feature.


Similarly, an adjective is usually immediately followed by the noun which it qualifies (for example, short discussion).

in case of both verbs and adjectives, the word immediately following (P 1 ) is likely to be a noun having strong syntactic relation to it.


However, when used in combination with other features, they may be useful.

like part of speech features, experiments were conducted using a combination of parse features in an effort to better capture the context and to identify sets of features which work well together.


Bigrams are ordered pairs of words that co-occur within five positions of each other.

co-occurrences are unordered word pairs that include the target word, whereas bigrams are ordered pairs that may or may not include the target.


A test instance can be represented by a second order context vector by finding the average of the first order context vectors that are associated with the words that occur near the target word.

the second order context representation relies on the first order context vectors of feature words.


Each row of the bigram matrix can be seen as a bigram vector that shows the scores of all bigrams in which the word represented by that row occurs as the first word.

the words representing the rows of the bigram matrix make the feature set while the words representing the columns form the dimensions of the feature space.


(Schütze, 1998) points out that single link clustering tends to place all instances into a single elongated cluster, whereas (Pedersen and Bruce, 1997) and (Purandare, 2003) show that hierarchical agglomerative clustering using average link (via McQuitty's method) fares well.

we have chosen to use average link/UPGMA as our criteria function for the agglomerative experiments.


N A V 7 1 2 bigram>co-occur PB 6 4 2 bigram<co-occur 1 1 0 bigram=co-occur 9 3 3 bigram>co-occur SC 4 1 1 bigram<co-occur 1 2 0 bigram=co-occur Table 2: Bigrams vs. Co-occurrences N A V PB 9 4 1 rbr>upgma 4 0 2 rbr<upgma 1 2 1 rbr=upgma SC 8 1 3 rbr>upgma 2 5 0 rbr<upgma 4 0 1 rbr=upgma Table 3: Repeated Bisections vs. UPGMA However, second order context vectors indirectly represent bigram features, and do not require an exact match between vectors in order to establish similarity.

the poor performance of bigrams in the case of first order context vectors suggests that when dealing with small amounts of data, we need to boost or enrich our bigram feature set by using some other larger training source like a corpus drawn from the Web.


However, the data specific to a given target word will capture the word usages in the immediate context of the target word.

we will test the hypothesis that a smaller sample of data where each instance includes the target word is more effective for sense discrimination than a more general corpus of training data.


The linguistic tradition of dependency grammar comprises a large and fairly diverse family of theories and formalisms that share certain basic assumptions about syntactic structure, in particular the assumption that syntactic structure consists of lexical nodes linked by binary relations called dependencies (see, e.g., Tesnière (1959), Sgall (1986), Mel'čuk (1988), Hudson (1990)).

the common formal property of dependency structures, as compared to the representations based on constituency (or phrase structure), is the lack of nonterminal nodes.


As it stands, this transition system is nondeterministic, since several transitions can often be applied to the same configuration.

in order to get a deterministic parser, we need to introduce a mechanism for resolving transition conflicts.


It is standard practice in data-driven approaches to natural language parsing to use treebanks both for training and evaluation.

the Penn Treebank of American English (Marcus et al., 1993) has been used to train and evaluate the best available parsers of unrestricted English text (Collins, 1999;Charniak, 2000).


For that purpose, we first define a function The first-order Taylor expansion of If fθ is of the following form: and if w and b are properly chosen as then (5) is approximately satisfied.

the TOP kernel is defined as A detailed discussion of the TOP kernel and its theoretical analysis have been given by Tsuda et al .


Thus, by preserving vectors d and − P (e)q(d|e) we can efficiently compute the dot-product in (39); the computational complexity of a kernel function is on the condition that the original dimension is larger than the number of categories.

from the viewpoint of computational time, our kernel has an advantage over some other kernels such as the PLSI-based Fisher kernel in Section 4, which requires the computational complexity of O(|I| × |C cluster |), where C cluster denotes the set of clusters.


For this reason, we regard the remaining categories other than the 10 most frequent categories as one category.

the model for negative examples is a mixture of 10 component models (9 out of the 10 most frequent categories and the new category consisting of the remaining categories).


training samples and development test samples in 'Flat', 'Manual', and 'Automatic' was 0.705, 0.720, and 0.782, respectively.

this is a difficult learning task and generalization to the test set is quite reasonable.


(For this example, we continue to assume that the full amount of The weight of each destination node w is calculated as the proportion of its score in the sum of the scores of its siblings.

in step 1 above, are both 1, and the full amount of E, F, and G are moved up.


The other method we use is the simple edge distance between nodes, w # h i k j P h .

4 far, we have defined SPD as a sum of propagated profile scores multiplied by the distance "travelled" (equation 1).


Let's return to the same example but redistribute some of the probability mass of : node E goes from a probability of 0.3 to 0.45, and node F goes from 0.2 to 0.05.

the distribution of the scores at the node B subtree is more skewed towards node E than in the original p ¡ ¤ £ ¥ q § © .


For instance, in the sentence "[ A1 The apple], said John, [ C−A1 is on the table]", the utterance argument (labeled with type A1) appears split into two phrases.

there is a set of non-overlapping arguments labeled with semantic roles associated with each proposition.


The second observation is that the arguments of all propositions of a sentence do not cross their boundaries, and that arguments of a particular proposition are usually found strictly within an argument of a higher level proposition.

the problem can be thought of as finding a hierarchy of arguments in which arguments are embedded inside others, and each argument is related to a number of propositions of a sentence in a particular role.


Semantic role labeling nevertheless differs from other tasks in which TBL has been successfully applied, such as part-of-speech tagging and named-entity recognition, because of the large span of some arguments, the dependence of argument labels on global information, and the fact that core argument labels are largely arbitrary.

some care is needed in posing the task in a TBL framework.


The POS sequence and length features showed no effect, and their omission even slightly improved performance.

the final system uses only eight features.


However, O is too frequently occurred than other semantic role labels, it can have a somewhat high probability than others.

to degrade its probability, we divide the single O into O-, O+, O0 with respect to the position of a constituent which is relative to the predicate.


Therefore, to degrade its probability, we divide the single O into O-, O+, O0 with respect to the position of a constituent which is relative to the predicate.

b-A*, I-A*, O-, O+, and O0 are used as semantic roles as shown in Figure 1.


For accurate semantic role labeling, we regard that the following information is important: the contextual information of the constituent, the syntactic information of the predicate, and the relation between the constituent and the predicate.

we use the features presented in Table 1 for semantic role labeling.


For example, agent is labeled as mainly A0 while benefactive can be labeled as A2 or A3.

the maximum entropy model can  Table 4: The results when the model is tested on the training set, the development set, and the test set get a good generalize performance in case of A0 or A1, but can't generalize well in other cases.


In this study, we try to apply SVMs to the semantic role labeling task, which is one of the multiclass problems.

we propose a two-phase semantic role labeling model which consists of the identification phase and the classification phase.


That is, since we find only the boundary of an argument in the identification phase, the number of classes is decreased into two (ARG, NON-ARG) or three (B-ARG, I-ARG, O).

we have to build only one or three SVM classifiers.


However, a syntactic constituent can be a chunk, or a clause.

we have to identify the boundaries of semantic arguments before we assign roles to the arguments.


Identifying a dependency relation is important for identifying a subject/object relation (S. Buchholz, 2002) and also for identifying the semantic arguments of a target predicate.

the features for finding dependency relations are implicitly represented in the feature set for the identification task.


Because we decide the unit of a constituent as a chunk or a subclause, words except the predicate in the target phrase 1 do not belong to constituent.

these words have to be handled independently.


Unfortunately, it turns out that it is difficult to predict the exact phrases accurately.

the goal of the first phase is to output a superset of the correct phrases by filtering out unlikely candidates.


The output of each stage becomes the initial annotation for the following stage.

our system only defines an explicit initial annotation for the verb-tagging phase: for each proposition, we initially tag only the single token containing the verb as V. The search for new transformation templates is terminated when no new transformation can be found that would improve the objective function by at least 0.03%.


Sparse data makes the direct estimation of È´ÊÀË ÄÀ Ëµ infeasible.

it is decomposed into several steps -first generating the head, then the right modifiers from the head outward, then the left modifiers in the same way.


If w 1 modifies w 2 , this implies that w 1 w 2 is a compound which in turn modifies w 3 , as in law enforcement agent.

the usefulness of the adjacency model vs. the dependency model can depend in part on the mix of left and right bracketing.


When a contextual token is observed, it is always counted as a single occurrence.

in contrast with earlier approaches (Sahlgren, 2001;Ehlert, 2003), we do not use a weighting scheme that is a function of distance from the reference token.


Whereas we exclude only at most 200 words in the highfrequency experiment, the number of terms excluded in the low-frequency experiment ranges from 939,496 (less than minimum frequency 3) to 1,534,427 (minimum frequency 100), out of a vocabulary containing about 1.6 million terms.

it is possible to reduce the expense of corpus analysis substantially without sacrificing semantic fidelity.


These (De Roeck et al., 2004a;De Roeck et al., 2004b) provide evidence of the fact that the "bag of words" assumption is invalid.

it sets the platform for a model that defies the independence assumption and considers the term distribution pattern in a document and corpus.


λ 1 determines the rarity of a term in a corpus, as it is the average gap at which the term occurs if it has not occurred recently.

a large value of λ 1 tells us that the term is very rare in the corpus and vice-versa.


Earn, Acquisition, Money-fx, Grain, Crude, Trade, Interest, Ship, Wheat and Corn, and we included in our dataset all the non empty documents labeled with at least one of those categories.

the final dataset includes 9295 document, of which 6680 are included in the training partition, and 2615 are in the test set.


(b) and (c) show that with the same clustering algorithm, different metrics could also produce different outcomes.

a good distance metric should be both domain-specific and associated with a specific clustering algorithm.


In the training stage, supervision is directly integrated into measuring the clustering error err S (h, p) of a partition function h by exploiting the feedback given by the true partition p. The goal of training is to find a partition function h * in a hypothesis space H that minimizes the error.

given a new data set S in the application stage, under some standard learning theory assumptions, the hope is that the learned partition function can generalize well and achieve small error as well.


Hence, in many cases learning from small data sets is highly desirable.

in this paper we also evaluate the performance of our algorithms on small amounts of training data and show their learning curve.


After classification, the start and end tags of the entities are obtained and need to be combined into one entity tag.

some post-processing is needed to guarantee tag consistency and to try to improve the results by exploring other information.


Table 4 shows that the PAUM had better results than both the standard Perceptron and the margin Perceptron Our conjecture was that the uneven margins parameter was more helpful on small training sets, because the smaller a training set is, the more imbalanced it could be.

we carried out experiments on a small numbers of training documents.


It is also worth noting that PAUM outperformed some other learning algorithms.

even PAUM on its own would be a good learning algorithm for IE.


For example, we anticipate that sentences with quotation marks will be problematic, as other researchers have observed that quoted text requires special handling for pronoun resolution (Kennedy and Boguraev, 1996).

we remove pronouns occurring in the same sentences as quotes from the learning process.


The relation Elaboration(153, h 2 ) in Figure 2 means that the segment 154 to 155 resolves to a proposition which elaborates part of the content of the proposition 153.

the twenty sixth in 154 resolves to the twenty sixth of July-any other interpretation contradicts the truth conditional consequences of Elaboration.


However, in order for the robot to successfully "give me the blue one," it must be able to link the meaning of the words in the utterance to its perception of the environment (Roy, Hsiao, & Mavridis, 2004).

recent work on grounding meaning has focused on how words and utterances map onto physical descriptions of the environment: either in the form of perceptual representations (Roy, in press, Siskind, 2001, Regier, 1996 or control schemas (Bailey, 1997Narayanan, 1999.


Here the ambiguity is based on the level of description that the speaker intended to convey.

while both meanings are correct (i.e., both meanings accurately describe the action), only one corresponds to the word "blicket."


In other words, the better one does without context, the less context improves performance.

we expect that in noisier environments (such as when speech recognition is employed) where channel probabilities are less reliable, employing intentional context will be even more advantageous.


This is particularly encouraging given the unconstrained nature of the speech on which it was trained.

even free and spontaneous speech can be handled when modeling a constrained domain of discourse.


The complexity of situated communication requires the use of domain-specific knowledge for modeling language use in different contexts.

with situated NLP systems, it is less productive to focus on how to scale up single models to operate beyond their original domains.


The induced positional model can model both of these situations, and can fit the true parses more closely as well (as evidenced by the fact that the likelihood of the data under the supervised positional model is higher than the likelihood under the supervised bigram model).

however, it is more sensitive to the initial parameter weights and learns to recreate the errors produced by the categorical parser.


An example are the transforms s/$/e/ and s/$/o/, both of which align members of a name cluster with other members of the same cluster (Clark/Clarke, Brook/Brooke, Robert/Roberto, etc.).

the system assigns the analysis tim => time to the word "time", suggesting that it be placed in the name cluster.


Comparing the full random set to the 100 sentences with the highest averaged KL-divergence, we can see that this is even more the case for the sub-set of 100 sentences (97.43%).

we can observe that  Table 1: Phrasal confusion matrices for document sub-set of 100 sentences sorted by average KLdivergence and for full random document sub-set of 1000 sentences (A1: Annotator 1, A2: Annotator 2).


However, as discussed in sections 2.3 and 2.4, we decided on sentences as the preferred annotation level.

it is important to explore these relationships at the sentence level as well.


Here, Charniak parser got 10.08% no matching arguments and Collins parser got 11.89%.

we can see that Charniak parser got a better result than Collins parser in the task of SRL.


The system makes use of partial and full syntactic information and converts the task into a sequential BIO-tagging.

the labeling architecture is very simple.


Also, we assumed that they exist in the uniform distance on the parse tree from the predicate's parent node (called P p ) to the parse constituent's parent node (called P c ).

for identifying semantic arguments, we do not need to examine all parse constituents in a parse tree.


This results in a list {(F i , t i )} of parse tree / SRL frame pairs, from which the reranker chooses.

our different reranking methods vary only in which parse tree is selected; given a parse tree, the frame is always chosen using the best prediction from the base model.


Besides, noisy information from unrelated parts of a sentence could also affect the training of machine learning models.

our system exploits the heuristic rules introduced by Xue and Palmer (2004) to filter out simple constituents that are unlikely to be arguments.


The goal of this ILP is to find a set of assignments for all z ia that maximizes the following function: Each S i ∈ S should have one of these argument types, or no type (null).

we have Next, we show how to transform the constraints in 1 http://homepages.inf.ed.ac.uk/s0450736/maxent_toolkit.html the filter function into linear equalities or inequalities, and use them in this ILP.


, Sj k on the same path in a full parsing tree, only one argument can be assigned to an argument type.

at least k − 1 arguments will be null, which is represented by φ in the following linear equality: Within the same sentence, A0-A5 cannot appear more than once.


Semantic parsing, identifying and classifying the semantic entities in context and the relations between them, potentially has great impact on its downstream applications, such as text summarization, question answering, and machine translation.

semantic parsing could be an important intermediate step for natural language comprehension.


(Gildea and Palmer, 2002;Pradhan et al., 2005;Punyakanok et al., 2004) However, these SRL systems generally extract features from sentences processed by a syntactic parser or other shallow parsing components, such as a chunker and a clause identifier.

the performance of the SRL systems relies heavily on those syntax-analysis tools.


Once the boundary of a constituent is defined, there is no way to change it in later phases.

the quality of the syntactic parser has a major impact on the final per-formance of an SRL system, and the percentage of correct constituents that is generated by the syntactic parser also defines the recall upper bound of an SRL system.


Then the edge ¥ ¥ k can be ruled out of the parse in one of two ways: it can be ruled out by making its score less than the adjacent scores as specified in (5), or it can be ruled out by making its score smaller than the score of .

the exact constraint contains a disjunction of two different constraints, which creates a non-convex constraint in w .


Nevertheless, the constraints in capture much of the relevant structure in the data, and are easy to enforce.


That is, we now only have a polynomial number of constraints corresponding to each word pair in (5), rather than the exponential number over every possible parse tree in (4).

we obtain a polynomial size quadratic program that can be solved for moderately large problems using standard software packages.


To be able to resolve the correct antecedent from the remaining potential antecedents, the system requires the knowledge that pseudoephedrine is a drug.

the problem is to create such a knowledge source and apply it to this task of antecedent selection.


5 Note that M I(X, Y ) = log P (X,Y ) P (X)P (Y ) and this is directly proportional to P (Y |X) = c(X,Y ) c(X) for a fixed Y .

we can simply use this conditional probability during implementation since the definite NP Y is fixed for the task of antecedent selection.


Then we asked a human judge (a native speaker of English) to predict a parent class of the antecedent that could act as a good definite anaphora choice in general, independent of a particular context.

the actual corpus sentence containing the antecedent and definite NP and its context was not provided to the judge.


Each of the corpus-based approaches was combined with WordNet resulting in two different models as follows: Given an antecedent X, the corpus-based approach looks up in its table the hypernym of X, for example Y, and only produces Y as the output if Y also occurs in the WordNet as hypernym.

wordNet is used as a filtering tool for detecting viable hypernyms.


Most of the methods heuristically extract features from parse trees, and from other sources, and use them in machine learning methods based on feature vector representation.

these methods depend on feature engineering, which is time-consuming.


In this study, we first evaluated the performance of the MOLT kernel for argument recognition, and found that the MOLT kernel cannot achieve a high accuracy if used in its original form.

in this paper we propose a modification of the MOLT kernel, which greatly improves the accuracy.


The problem with the original MOLT kernel is that it treats subtrees with one mark, i.e., those including only the argument or the predicate node, and subtrees with two marks, i.e., those including both the argument and the predicate nodes equally, although the latter is likely to be more important for distinguishing difficult arguments.

we modified the MOLT kernel so that the marks can be weighted in order to give large weights to the subtrees with many marks.


Since arguments are defined for a given predicate, this classification is the recognition of a relation between the predicate and tree nodes.

we want to build a binary classifier that returns a +1 for correct relations and a -1 for incorrect relations.


[ As can be seen, "he" is the A0 argument of "say", but not an argument of "build".

our classifier should return a +1 for the tree where "he" is marked when the predicate is "say", and a -1 when the predicate is "build".


As described in (Collins and Duffy, 2002), ∆ can be computed in Traditional semantic role labeling systems extract features from pairs of nodes corresponding to a predicate and one of its argument, respectively.

they focus on only binary relations to make classification decisions.


The re-ranking approach is the most promising one as suggested in (Toutanova et al., 2005) but it does not clearly reveal if tree kernels can be used to learn the difference between correct or incorrect argument structures.

it is interesting to study both the above approaches.


Additionally, it should be noted that the semantic information provided by the role type can remarkably help the detection of correct or incorrect predicate argument structures.

we can enrich the argument node label with the role type, e.g.


As the number of such structures is very large the SVM training time was very high.

we sped up the learning process by using only the AST s associated with the core arguments.


Given a set of WSD problems and their respective labeled examples, we seek to improve overall performance on that set by using all the labeled examples (irrespective of target words) for the entire set in learning a disambiguator for each individual problem.

in effect, on each individual problem (e.g., disambiguation of "art") we benefit from training examples for other problems (e.g., disambiguation of "bar", "canal", and so forth).


It is also noteworthy that U-DOP does not employ a separate class for non-constituents, so-called distituents, while CCM does.

good results can be obtained without keeping track of distituents but by simply assigning all binary trees to the strings and letting the DOP model decide which substrings are most likely to form constituents.